Bakshi Abhisek, Gangopadhyay Kaustav, Basak Sujit, De Rajat K, Sengupta Souvik, Dasgupta Abhijit
Department of Research and Development, Michelin India Private Limited Pune, India.
Department of Structural Biology, St. Jude Children's Research Hospital Memphis, United States.
J Am Med Inform Assoc. 2025 Feb 18. doi: 10.1093/jamia/ocaf035.
This study addresses the significant challenges posed by emerging SARS-CoV-2 variants, particularly in developing diagnostics and therapeutics. Drug repurposing is investigated by identifying critical regulatory proteins impacted by the virus, providing rapid and effective therapeutic solutions for better disease management.
We employed a comprehensive approach combining mathematical modeling and efficient parameter estimation to study the transient responses of regulatory proteins in both normal and virus-infected cells. Proportional-integral-derivative (PID) controllers were used to pinpoint specific protein targets for therapeutic intervention. Additionally, advanced deep learning models and molecular docking techniques were applied to analyze drug-target and drug-drug interactions, ensuring both efficacy and safety of the proposed treatments. This approach was applied to a case study focused on the cytokine storm in COVID-19, centering on Angiotensin-converting enzyme 2 (ACE2), which plays a key role in SARS-CoV-2 infection.
Our findings suggest that activating ACE2 presents a promising therapeutic strategy, whereas inhibiting AT1R seems less effective. Deep learning models, combined with molecular docking, identified Lomefloxacin and Fostamatinib as stable drugs with no significant thermodynamic interactions, suggesting their safe concurrent use in managing COVID-19-induced cytokine storms.
The results highlight the potential of ACE2 activation in mitigating lung injury and severe inflammation caused by SARS-CoV-2. This integrated approach accelerates the identification of safe and effective treatment options for emerging viral variants.
This framework provides an efficient method for identifying critical regulatory proteins and advancing drug repurposing, contributing to the rapid development of therapeutic strategies for COVID-19 and future global pandemics.
本研究应对新型严重急性呼吸综合征冠状病毒2（SARS-CoV-2）变体带来的重大挑战，尤其是在开发诊断方法和治疗方法方面。通过识别受病毒影响的关键调节蛋白来研究药物重新利用，为更好地管理疾病提供快速有效的治疗方案。
我们采用了一种综合方法，将数学建模和高效参数估计相结合，以研究正常细胞和病毒感染细胞中调节蛋白的瞬态反应。使用比例积分微分（PID）控制器来确定治疗干预的特定蛋白质靶点。此外，应用先进的深度学习模型和分子对接技术来分析药物-靶点和药物-药物相互作用，确保所提议治疗方法的有效性和安全性。该方法应用于一个以新型冠状病毒肺炎（COVID-19）细胞因子风暴为重点的案例研究，以在SARS-CoV-2感染中起关键作用的血管紧张素转换酶2（ACE2）为核心。
我们的研究结果表明，激活ACE2是一种有前景的治疗策略，而抑制血管紧张素Ⅱ1型受体（AT1R）似乎效果较差。深度学习模型与分子对接相结合，确定洛美沙星和福斯替尼为稳定药物，没有明显的热力学相互作用，表明它们可安全联合用于治疗COVID-19引起的细胞因子风暴。
结果突出了激活ACE2在减轻SARS-CoV-2引起的肺损伤和严重炎症方面的潜力。这种综合方法加快了针对新型病毒变体的安全有效治疗方案的识别。
该框架为识别关键调节蛋白和推进药物重新利用提供了一种有效方法，有助于COVID-19及未来全球大流行治疗策略的快速发展。
